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Overview:
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Understanding the builds effectively used by players in RPG looter online games is key for designers and other stakeholders; but due to combinatorial explosion inherent to the nature of data, traditional unsupervised machine learning approaches often fail at extracting meaningful and actionable results.nnWe propose a novel and original method - called "BaT" - to automatically segment high volumes of player in-game data into meaningful player builds, that produce easily interpretable builds clusters in a fast, efficient, and scalable way.
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